E3S Web Conf.
Volume 252, 20212021 International Conference on Power Grid System and Green Energy (PGSGE 2021)
|Number of page(s)||6|
|Section||Research and Development of Electrical Equipment and Energy Nuclear Power Devices|
|Published online||23 April 2021|
- Ding Y W. Extraction of winter wheat planting area based on MODIS image data in northern Anhui Province [D]. Anhui University, 2019. [Google Scholar]
- Zhang X W, Chen Y S, Meng Q, Wang X. Crop phenology extraction based on time series MODIS NDVI [J]. Chinese Agricultural Science Bulletin, 2018, 34(20): 158–164. [Google Scholar]
- Xu QY, Yang G J, Long H L, Wang CC, LiX C, Huang D C. Crop planting identification based on MODISNDVI multi-year time series data [J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(11): 134–144. [Google Scholar]
- Jin Z T, Li W G, Jing Y S. Research on the Appropriate Scale of Winter Wheat Planting Area Extraction Based on Image Fusion [J]. Jiangsu Journal of Agricultural Sciences, 2015, 31(06): 1312–1317. [Google Scholar]
- Ji X S, Li X, Wan Z F, Yao X Zhu Y, Cheng T. Classification of cotton and jujube trees in Alar city of Xinjiang based on high spatial resolution satellite image [J]. Agricultural Sciences in China, 2019, 52(06): 997–1008. [Google Scholar]
- Yang H W, Fang J Y, Zhao D. Feature selection for fine classification of crops by remote sensing based on improved separation threshold method [J]. Journal of Applied Sciences, 2019, 37(01): 51–63. [Google Scholar]
- Liu Z H, Liu L, Guo H, Cheng P. The extraction of spring wheat based on GF1-NDVI time series image was studied [J]. Beijing surveying and mapping, 2018, 32(06): 643–646. [Google Scholar]
- Zhao Z, Yan L. 16 -band image spectral feature classification of Worldview satellite [J]. Remote sensing information, 2019, 34(01): 36–43. [Google Scholar]
- Bi K Y, Niu Z, Huang N, Kang J, Pei J. Vegetation identification based on Sentinel-2a time series data and object-oriented decision tree method [J]. Geography and Geographic Information Science, 2017, 33(05): 16–20+27+127. [Google Scholar]
- Wang L J, Guo Y, He J, Wang L M, Zhang X W, Liu T. A Sentinel-2a Image Crop Extraction Method Based on Decision Tree and SVM [J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(09): 146–153. [Google Scholar]
- Fang C Y, Wang L, Xu H Q. Comparative study of red edge index of different vegetation in health discrimination of urban grassland [J]. Journal of Geo-information Science, 2017, 19(10): 1382–1392. [Google Scholar]
- Zhang P, Ma X Y, Zhang L, Xing X G. Land use classification in Guanzhong region based on different vegetation indices and classification regression trees [J]. Soil and Water Conservation Research, 2018, 25(03): 310–316. [Google Scholar]
- Belgiu M, Drăguţ L. Random forest in remote sensing: A review of applications and future directions[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 114: 24–31. [Google Scholar]
- Gislason P O, Benediktsson J A, Sveinsson J R. Random forest classification of multisource remote sensing and geographic data[C]. 2004 IEEE International Geoscience and Remote Sensing Symposium, 2004, 2: 1049–1052. [Google Scholar]
- Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing and Environment, 1979, 8(2): 127–150. [Google Scholar]
- Major D J, Frédéric Baret, Guyot G. A ratio vegetation index adjusted for soil brightness[J]. International Journal of Remote Sensing, 1990, 11(5): 727–740. [Google Scholar]
- Schell J A, Deering D. Monitoring vegetation systems in the Great Plains with ERTS[J]. NASA Special Publication, 1973, 351: 309. [Google Scholar]
- Gitelson A A, Merzlyak M N. Remote sensing of chlorophyll concentration in higher plant leaves[J]. Advances in Space Research, 1998, 22(5): 689–692. [Google Scholar]
- Qi J, Chehbouni A, Huete A R, et al. A modified soil adjusted vegetation index[J]. Remote sensing of environment, 1994, 48(2): 119–126. [Google Scholar]
- Christos E and Alexandre N. Red-Edge Normalized Difference Vegetation Index (NDVI705) from Sentinel-2 imagery to assess post-fire regeneration [J]. Remote Sensing Applications: Society and Environment, 2020, 17: 100283. [Google Scholar]
- Frampton W J, Dash J, Watmough G, et al. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation[J]. ISPRS journal of photogrammetry and remote sensing, 2013, 82: 83–92. [Google Scholar]
- Haboudane D, Miller J R, Pattey E, et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture[J]. Remote Sensing of Environment, 2004, 90(3): 337–352. [Google Scholar]
- Gao B C. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space[J]. Remote sensing of environment, 1996, 58(3): 257–266. [Google Scholar]
- Wang Z M, Du B, Zhang L P, et al. Hyperspectral image classification based on texture feature and morphological feature fusion [J]. Journal of photons, 2014, 43(08): 122–129. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.